House Rent Prediction

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

df = pd.read_csv(r"C:\Users\manav\Downloads\CSV's\House rent\House_Rent_Dataset.csv")

df.head()

	Posted On	BHK	Rent	Size	Floor	Area Type	Area Locality	City	Furnishing Status	Tenant Preferred	Bathroom	Point of Contact
0	2022-05-18	2	10000	1100	Ground out of 2	Super Area	Bandel	Kolkata	Unfurnished	Bachelors/Family	2	Contact Owner
1	2022-05-13	2	20000	800	1 out of 3	Super Area	Phool Bagan, Kankurgachi	Kolkata	Semi-Furnished	Bachelors/Family	1	Contact Owner
2	2022-05-16	2	17000	1000	1 out of 3	Super Area	Salt Lake City Sector 2	Kolkata	Semi-Furnished	Bachelors/Family	1	Contact Owner
3	2022-07-04	2	10000	800	1 out of 2	Super Area	Dumdum Park	Kolkata	Unfurnished	Bachelors/Family	1	Contact Owner
4	2022-05-09	2	7500	850	1 out of 2	Carpet Area	South Dum Dum	Kolkata	Unfurnished	Bachelors	1	Contact Owner

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4746 entries, 0 to 4745
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   Posted On          4746 non-null   object
 1   BHK                4746 non-null   int64 
 2   Rent               4746 non-null   int64 
 3   Size               4746 non-null   int64 
 4   Floor              4746 non-null   object
 5   Area Type          4746 non-null   object
 6   Area Locality      4746 non-null   object
 7   City               4746 non-null   object
 8   Furnishing Status  4746 non-null   object
 9   Tenant Preferred   4746 non-null   object
 10  Bathroom           4746 non-null   int64 
 11  Point of Contact   4746 non-null   object
dtypes: int64(4), object(8)
memory usage: 445.1+ KB

df.isna().sum()

Posted On            0
BHK                  0
Rent                 0
Size                 0
Floor                0
Area Type            0
Area Locality        0
City                 0
Furnishing Status    0
Tenant Preferred     0
Bathroom             0
Point of Contact     0
dtype: int64

There is no null values for any column in the dataset

df.describe()

	BHK	Rent	Size	Bathroom
count	4746.000000	4.746000e+03	4746.000000	4746.000000
mean	2.083860	3.499345e+04	967.490729	1.965866
std	0.832256	7.810641e+04	634.202328	0.884532
min	1.000000	1.200000e+03	10.000000	1.000000
25%	2.000000	1.000000e+04	550.000000	1.000000
50%	2.000000	1.600000e+04	850.000000	2.000000
75%	3.000000	3.300000e+04	1200.000000	2.000000
max	6.000000	3.500000e+06	8000.000000	10.000000

PairPlot of Data

sns.pairplot(df,height=4)

<seaborn.axisgrid.PairGrid at 0x247565088e0>

Univariate Analysis

Rent (our target value)

fig = px.histogram(df,x='Rent',color_discrete_sequence = px.colors.qualitative.Set3, title='Rent Prices Distribution Histogram')
fig.show()
fig = px.box(df, x='Rent', title = 'Boxplot For Rent Prices')
fig.show()

max_value= df.max()

max_value

Posted On                         2022-07-11
BHK                                        6
Rent                                 3500000
Size                                    8000
Floor                Upper Basement out of 9
Area Type                         Super Area
Area Locality                     whitefield
City                                  Mumbai
Furnishing Status                Unfurnished
Tenant Preferred                      Family
Bathroom                                  10
Point of Contact               Contact Owner
dtype: object

Dropping Outliers

print(np.where(df['Rent']>2000000))

(array([1837], dtype=int64),)

df.drop([1837], axis=0,inplace=True)
fig=px.box(df,x='Rent',title='Boxplot For Rent Prices')
fig.show()

df['BHK'].value_counts()

2    2265
1    1167
3    1097
4     189
5      19
6       8
Name: BHK, dtype: int64

sns.set_style('whitegrid')
fig,axes=plt.subplots(figsize=(12,8))
colors=['#87ace8','#e3784d', '#6ecc64','#b644e3','#eb7c87', '#EAE509']

ax = sns.countplot(x='BHK',data=df,palette=['#e3784d','#87ace8', '#6ecc64','#b644e3','#eb7c87','#EAE509'])
for container in ax.containers:
    ax.bar_label(container)
plt.title('Frequency of different number of BHKs present in Houses available for Rent',fontsize=15)
plt.show()

fig = px.pie(df, names='BHK', height=450, width= 450, color_discrete_sequence=px.colors.sequential.deep, title='Pie Chart for different number of BHKs present in Houses available for Rent')
fig.update_traces(textfont_size=15)
fig.show()

Observations:

Mostly 2 BHK house are rented the most, as it is mostly under budget for the people and can be rented soon.

5 and 6 BHK house are put less for rent, because there is very less demand for such big spacious houses in India.

Bathrooms

df['Bathroom'].value_counts()

2     2291
1     1474
3      748
4      156
5       60
6       12
7        3
10       1
Name: Bathroom, dtype: int64

sns.set_style("whitegrid")
fig,axes = plt.subplots(figsize=(15,8))
colors = ['#87ace8','#e3784d', '#6ecc64','#b644e3','#eb7c87', '#EAE509','#fab1f3','#86f7d7']
ax = sns.countplot(x='Bathroom',data=df, palette=['#e3784d','#87ace8', '#6ecc64','#b644e3','#eb7c87','#fab1f3','#86f7d7','#EAE509'])
for container in ax.containers:
    ax.bar_label(container)
plt.title('Frequency of different number of Bathrooms present in Houses available for Rent',fontsize=15)
plt.show()

fig = px.pie(df, names='Bathroom', height=450, width= 650, color_discrete_sequence=px.colors.sequential.deep, title='Pie Chart for different number of Bathrooms present in Houses available for Rent')
fig.update_traces(textfont_size=15)
fig.show()

Observations

Houses with 2 bathrooms are most common for the houses put up on rent

Housese with 7 and 10 bathroom quite seems inappropriate and not much of use.

---

City

df['City'].value_counts()

Mumbai       972
Chennai      891
Bangalore    885
Hyderabad    868
Delhi        605
Kolkata      524
Name: City, dtype: int64

sns.set_style('whitegrid')
fig,axes = plt.subplots(figsize=(12,8))
colors = ['#87ace8','#e3784d', '#6ecc64','#b644e3','#eb7c87', '#EAE509']

ax = sns.countplot(x='City',data=df,palette=['#EAE509','#87ace8', '#6ecc64','#eb7c87','#e3784d','#b644e3'])
for container in ax.containers:
    ax.bar_label(container)

plt.title('City wise Houses available for Rent',fontsize=15)
plt.show()

fig=px.pie(df,names='City',height=450,width=650,color_discrete_sequence=px.colors.sequential.Sunsetdark, title='Pie Chart for houses available for rent in different cities')
fig.update_traces(textfont_size=15)
fig.show()

Observation

Mumbai, followed by Chennai and Hydrabad has most number of rented houses, seems like there is very high demand considering the job corporates and other factors

---

Area Type

df['Area Type'].value_counts()

Super Area     2446
Carpet Area    2297
Built Area        2
Name: Area Type, dtype: int64

colors = ['#87ace8', '#6ecc64', '#EAE509']
sns.set_style('whitegrid')
fig,axes = plt.subplots(figsize=(12,8))

ax = sns.countplot(x='Area Type',data=df, palette=['#87ace8', '#6ecc64', '#EAE509'])
for container in ax.containers:
    ax.bar_label(container)
plt.title('Area wise rented houses distribution',fontsize=15)
plt.show()

Observations

Super area houses are more put up on rent

Built area wise houses are rented less

---

Point Of Contact

df['Point of Contact'].value_counts()

Contact Owner      3216
Contact Agent      1528
Contact Builder       1
Name: Point of Contact, dtype: int64

sns.set_style('whitegrid')
fig,axes = plt.subplots(figsize=(10,5))
colors = ['#885122', '#7f557e', '#EAE509']
ax= sns.countplot(x='Point of Contact',data=df,palette = ['#885122', '#7f557e', '#EAE509'])
for container in ax.containers:
    ax.bar_label(container)
plt.title('Point Of Contact Distribution For House Renting')
plt.show()

Observation

Contact wise most have put the contact of owner only, as it directly more helps with negotiation and verification.

---

Tenant Preferred

df['Tenant Preferred'].value_counts()

Bachelors/Family    3444
Bachelors            829
Family               472
Name: Tenant Preferred, dtype: int64

sns.set_style('whitegrid')
fig,axes = plt.subplots(figsize=(12,5))
color=['lightblue', 'lightgreen', 'yellow']
ax = sns.countplot(x='Tenant Preferred',data=df,palette = sns.dark_palette("red", n_colors=3))
for container in ax.containers:
    ax.bar_label(container)
plt.title('Tenant Preferred Distribution for House Renting',fontsize=15)
plt.show()

Observation

It seems most rented house have no special category , they are comfortable with both bachelors and family.

---

Furniture

df['Furnishing Status'].value_counts()

Semi-Furnished    2250
Unfurnished       1815
Furnished          680
Name: Furnishing Status, dtype: int64

sns.set_style('whitegrid')
fig,axes=plt.subplots(figsize=(12,8))
colors = sns.color_palette('Set1')

ax= sns.countplot(x='Furnishing Status',data=df,palette = sns.color_palette('Set1'))
for container in ax.containers:
    ax.bar_label(container)
plt.title('Furnishing Status Distribution for house renting',fontsize=15)
plt.show()

Observations:

1) Most rented houses are Semi-Furnished.

2) Also these houses are pocket friendly.

---

Sizes of Appartments in sq. feet

df['Size'].value_counts()

1000    240
600     225
800     220
1200    193
500     192
       ... 
2770      1
1252      1
904       1
721       1
855       1
Name: Size, Length: 615, dtype: int64

fig = px.histogram(df,x='Size',title='Size distribution')
fig.show()

fig = px.box(df,x='Size', title= 'Boxplot for Size')
fig.show()

Observations:

The smallest house rented has a size of about 10 sq. feet.

The biggest house rented has a size of about 8000 sq. feet.

The Median of house rented has a size of about 850 sq. feet.

---

City and Rent Distribution

fig, ax = plt.subplots(figsize=(15,10))
sns.stripplot(x="City", y='Rent', data=df, jitter=True)
plt.title('Scattered Rent Distribution by City', fontsize=15)
plt.xticks(rotation=45)
plt.show()

Observations

1) This combination of economic, social, and geographic factors makes Mumbai a hotspot for rental housing, with high demand often translating to steep prices.

2) Cities like Banglore, Delhi, Chennai, Hyderabad having equal amount of rent.

3) Kolkata is not that flourished in terms of job opportunities and life style as compared to other cities.

---

Sunburst Chart

fig = px.sunburst(df, path=['City','Area Type', 'Furnishing Status', 'Tenant Preferred'], width=900,
    height=900,title='Allotment of flats according to Bachelors/Family/(Bachelors/Family)',color_discrete_sequence=px.colors.cyclical.Phase)
fig.show()

##Dropping unnecesaary columns from dataset
rent_data = df.drop(['Posted On','Area Locality','Floor'],axis=1)
rent_data.head()

	BHK	Rent	Size	Area Type	City	Furnishing Status	Tenant Preferred	Bathroom	Point of Contact
0	2	10000	1100	Super Area	Kolkata	Unfurnished	Bachelors/Family	2	Contact Owner
1	2	20000	800	Super Area	Kolkata	Semi-Furnished	Bachelors/Family	1	Contact Owner
2	2	17000	1000	Super Area	Kolkata	Semi-Furnished	Bachelors/Family	1	Contact Owner
3	2	10000	800	Super Area	Kolkata	Unfurnished	Bachelors/Family	1	Contact Owner
4	2	7500	850	Carpet Area	Kolkata	Unfurnished	Bachelors	1	Contact Owner

!pip install nbconvert

Requirement already satisfied: nbconvert in c:\users\manav\anaconda3\lib\site-packages (6.1.0)
Requirement already satisfied: testpath in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.5.0)
Requirement already satisfied: bleach in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (4.0.0)
Requirement already satisfied: jupyterlab-pygments in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.1.2)
Requirement already satisfied: pandocfilters>=1.4.1 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (1.4.3)
Requirement already satisfied: defusedxml in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.7.1)
Requirement already satisfied: entrypoints>=0.2.2 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.3)
Requirement already satisfied: pygments>=2.4.1 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (2.10.0)
Requirement already satisfied: jupyter-core in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (4.8.1)
Requirement already satisfied: jinja2>=2.4 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (3.1.3)
Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.5.3)
Requirement already satisfied: traitlets>=5.0 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (5.1.0)
Requirement already satisfied: mistune<2,>=0.8.1 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (0.8.4)
Requirement already satisfied: nbformat>=4.4 in c:\users\manav\anaconda3\lib\site-packages (from nbconvert) (5.1.3)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\manav\anaconda3\lib\site-packages (from jinja2>=2.4->nbconvert) (2.1.5)
Requirement already satisfied: jupyter-client>=6.1.5 in c:\users\manav\anaconda3\lib\site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert) (6.1.12)
Requirement already satisfied: async-generator in c:\users\manav\anaconda3\lib\site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert) (1.10)
Requirement already satisfied: nest-asyncio in c:\users\manav\anaconda3\lib\site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert) (1.5.1)
Requirement already satisfied: python-dateutil>=2.1 in c:\users\manav\anaconda3\lib\site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert) (2.8.2)
Requirement already satisfied: pyzmq>=13 in c:\users\manav\anaconda3\lib\site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert) (22.2.1)
Requirement already satisfied: tornado>=4.1 in c:\users\manav\anaconda3\lib\site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert) (6.1)
Requirement already satisfied: pywin32>=1.0 in c:\users\manav\anaconda3\lib\site-packages (from jupyter-core->nbconvert) (228)
Requirement already satisfied: ipython-genutils in c:\users\manav\anaconda3\lib\site-packages (from nbformat>=4.4->nbconvert) (0.2.0)
Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\users\manav\anaconda3\lib\site-packages (from nbformat>=4.4->nbconvert) (3.2.0)
Requirement already satisfied: six>=1.11.0 in c:\users\manav\anaconda3\lib\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert) (1.16.0)
Requirement already satisfied: attrs>=17.4.0 in c:\users\manav\anaconda3\lib\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert) (21.2.0)
Requirement already satisfied: pyrsistent>=0.14.0 in c:\users\manav\anaconda3\lib\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert) (0.18.0)
Requirement already satisfied: setuptools in c:\users\manav\anaconda3\lib\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.4->nbconvert) (58.0.4)
Requirement already satisfied: packaging in c:\users\manav\anaconda3\lib\site-packages (from bleach->nbconvert) (21.0)
Requirement already satisfied: webencodings in c:\users\manav\anaconda3\lib\site-packages (from bleach->nbconvert) (0.5.1)
Requirement already satisfied: pyparsing>=2.0.2 in c:\users\manav\anaconda3\lib\site-packages (from packaging->bleach->nbconvert) (3.0.4)